Automated RSI Trading Strategy
Algorithmic trading bot using RSI indicators for optimal entry and exit signals
Hero Summary
What
This automated trading system uses the Relative Strength Index (RSI) to identify potential trading opportunities in volatile stocks. The bot monitors market conditions in real-time and executes trades based on predefined RSI thresholds, ensuring consistent application of the strategy without emotional bias.
Why
The strategy enters long positions when RSI drops below 30 (oversold condition) and exits when RSI rises above 70 (overbought condition). Risk management is implemented through stop-loss orders at 5% below entry price and position sizing based on account equity. The system includes filters to avoid trading during low-volume periods and market gaps.
Result
annual Return
9.15%
win Rate
84.3%
sharpe Ratio
0.73
profit Factor
3.88
System Overview
How It Works
The strategy enters long positions when RSI drops below 30 (oversold condition) and exits when RSI rises above 70 (overbought condition). Risk management is implemented through stop-loss orders at 5% below entry price and position sizing based on account equity. The system includes filters to avoid trading during low-volume periods and market gaps.
Technologies Used
Technical Implementation
Built using Python with the Backtrader framework for backtesting and live trading capabilities. Data is sourced from Interactive Brokers API with real-time price feeds. The system uses Pandas for data manipulation, NumPy for calculations, and Matplotlib for performance visualization. Trade execution is handled through the broker's API with proper error handling and logging.
Performance & Risk Metrics
10-Stock Portfolio
AAPL · AMZN · GOOGL · JPM · META · MSFT · NVDA · TSLA · UNH · V — $1,000 each
Test Period
Jan 2015 – Dec 2024
83 trades
Initial Capital
$10,000.00
Final Value
$23,958.35
Net Profit
$13,958.35
Annual Return
9.15%
Risk-Adjusted Metrics
Sharpe Ratio
0.73
Risk-adjusted return
Sortino Ratio
0.83
Downside risk focus
Calmar Ratio
0.35
Return vs drawdown
Trading Performance
Win Rate
84.34%
Profit Factor
3.88
Max Drawdown
-26.21%
Avg Trade
$168.17
Expectancy / Trade
+11.84%
Avg Hold
138.0 days
Trade Examples & Visualizations
Interactive backtests showing every buy and sell signal against price and RSI. Drag across a chart to zoom in, double-click to reset.
Limitations & Failure Modes
Every strategy has weaknesses. Here are the known limitations and scenarios where this system struggles.
Handling API rate limits during backtesting
Optimizing RSI parameters without overfitting
Managing execution latency in live trading
Key Learnings
The most important lesson was the impact of transaction costs on profitability. Initial backtests showed higher returns, but realistic commission and slippage assumptions brought returns down significantly. I also learned the importance of testing across different market conditions - the strategy performs well in trending markets but struggles in choppy, sideways markets. Future versions will include market regime detection.
Future Improvements
Planned enhancements and next steps for this project.
Add machine learning for dynamic RSI threshold adjustment
Implement multi-timeframe analysis
Create web dashboard for monitoring performance
Add support for multiple assets simultaneously